# -*- coding: UTF-8 -*-
# Create by YangZhang on 2022/8/8
import time

import torch
import torchvision
from torch.utils import data
from torchvision import transforms

# 通过ToTensor实例将图像数据从PIL类型变换成32位浮点数格式，
# 并除以255使得所有像素的数值均在0到1之间
trans = transforms.ToTensor()
mnist_train = torchvision.datasets.FashionMNIST(
    root="./data", train=True, transform=trans, download=True)
mnist_test = torchvision.datasets.FashionMNIST(
    root="./data", train=False, transform=trans, download=True)

print(len(mnist_train), len(mnist_test))
print(mnist_train[0][0].shape)


def get_fashion_mnist_labels(labels):  # @save
    """返回Fashion-MNIST数据集的文本标签"""
    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',
                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
    return [text_labels[int(i)] for i in labels]


# def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  # @save
#     """绘制图像列表"""
#     figsize = (num_cols * scale, num_rows * scale)
#     _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
#     axes = axes.flatten()
#     for i, (ax, img) in enumerate(zip(axes, imgs)):
#         if torch.is_tensor(img):
#             # 图片张量
#             ax.imshow(img.numpy())
#         else:
#             # PIL图片
#             ax.imshow(img)
#         ax.axes.get_xaxis().set_visible(False)
#         ax.axes.get_yaxis().set_visible(False)
#         if titles:
#             ax.set_title(titles[i])
#     return axes


X, y = next(iter(data.DataLoader(mnist_train, batch_size=18)))

print(X, y)
print(X.shape, y.shape)
# show_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y))

batch_size = 256


# windows 设置num_workers大概率会报错
def get_dataloader_workers():  # @save
    """使用4个进程来读取数据"""
    return 1


train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True)
# timer = d2l.Timer()
timer = time.time()
for X, y in train_iter:
    continue
timer2 = time.time()
print(f'{timer2 - timer:.2f} sec')


